Abstract

Hyperspectral imaging systems for daylight operation measure and analyze reflected and scattered radiation in p-spectral channels covering the reflective infrared region 0.4–2.5μm. Consequently, the p-dimensional joint distribution of background clutter is required to design and evaluate optimum hyperspectral imaging processors. In this paper, we develop statistical models for the spectral variability of natural hyperspectral backgrounds using the class of elliptically contoured distributions. We demonstrate, using data from the NASA AVIRIS sensor, that models based on the multivariate t-elliptically contoured distribution capture with sufficient accuracy the statistical characteristics of natural hyperspectral backgrounds that are relevant to target detection applications.

Strictly speaking Δ is the Mahalanobis distance and Δ2 is the squared Mahalanobis distance; however, for simplicity, the term Mahalanobis distance is used in both cases. The exact meaning should be clear from the context.

Strictly speaking Δ is the Mahalanobis distance and Δ2 is the squared Mahalanobis distance; however, for simplicity, the term Mahalanobis distance is used in both cases. The exact meaning should be clear from the context.

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